Economic allocation of test times for subsystem-level reliability growth testing

In the development of new electronic systems the planning of reliability growth tests has become both more critical and more difficult as available testing budgets have diminished. Previously, system designers were able to plan and implement relatively lengthy reliability growth test plans to assist in the development of reliable systems. A new method is presented to allocate subsystem reliability growth test time in order to maximize the system mean time between failure (MTBF) or system reliability when designers are confronted with limited testing resources. For certain problems, the algorithm yields the same results as competing approaches to the problem but with significantly fewer required iterations. More significantly, the new algorithm applies to a larger problem domain compared to analogous algorithms. Much more realistic formulations of the problem can now be solved optimally. The algorithm is based on objective function gradient information projected onto a feasible region that consists of candidate test plans given the testing budget constraint. The algorithm is demonstrated on several examples with superior results.     

Sensitivity analysis of the inverted slider crank straight-line generatorAnalyse de sensibilite du generateur de lignes droites par bielle a coulisse invertie

This paper presents the results of a study of an inverted slider crank straight-line generator and its sensitivity to link-length tolerances. Analytical results are presented graphically using a Tektronix CRT to assist the designer in making intelligent choices for tolerancing the links, based on his performance requirements.     

Penalty guided genetic search for reliability design optimization

Reliability optimization has been studied in the literature for decades, usually using a mathematical programming approach. Because of these solution methodologies, restrictions on the type of allowable design have been made, however heuristic optimization approaches are free of such binding restrictions. One difficulty in applying heuristic approaches to reliability design is the highly constrained nature of the problems, both in terms of number of constraints and the difficulty of satisfying constraints. This paper presents a penalty guided genetic algorithm which efficiently and effectively searches over promising feasible and infeasible regions to identify a final, feasible optimal, or near optimal, solution. The penalty function is adaptive and responds to the search history. Results obtained on 33 test problems from the literature dominate previous solution techniques.